Abdominal organ segmentation is a very fundamental and important task in abdominal disease diagnosis, cancer treatment, and radiotherapy planning [1]}. As accurately segmented organs can provide pieces of valuable information for the clinical diagnosis and follow-ups like organ size, location, boundary state, and spatial relationship of multiple organs, etc. In addition, organs segmentation also plays an irreplaceable role in clinical treatment, especially in radiation therapy-based cancer and oncology treatments [2]}. Accurate organs at risk segmentation can alleviate potential effects on healthy organs nears cancer regions. However, in clinical practices, organs segmentation is usually performed by radiation oncologists or radiologists manually. It is time-consuming and error-prone, as it requires annotators to delineate and check slice-by-slice and may take several hours per case. In addition, due to the different imaging protocols/quality, and anatomical structures, fast delineation of a large number of organs is also a challenging task for junior oncologists [3]}.
